Enterprise AI Strategies Theater

Tuesday December 4, 2018

1:00 PANEL: AI in Government

As IT-based enterprise organizations deploy intelligent automation across their business and industries, the opportunity emerges for federal, state, and local government agencies to become fast-followers. Whether in the pursuit of delivering enhanced
services to constituents, increasing worker productivity, reducing operational cost, or merely accelerating the digital transformation efforts underway within the agency. Attendees to this panel will hear:

How agencies are utilizing automation and machine learning to improve service delivery and response time

The unique challenges faced by public sector IT organizations compared to private enterprise

The evolving discussion around AI ethics, safety, and regulatory requirements

Application integration services involve any work done to link or integrate multiple AI technologies together, as well as the work completed to tie in AI software with existing software or systems. AI can only provide significant benefits
when it is deployed seamlessly, with potential process interruptions (such as exceptions) identified and managed prior to full implementation. Much of the work done may revolve around the modification of existing systems, processes, and
job functions to ensure that the new AI technology can enhance speed, accuracy, or productivity, and seamlessly sit within or alongside of existing, mission-critical applications.

What market drivers and conditions are driving the market for AI system integration services?

How should AI budgets for AI integration be structured and managed to ensure optimal efficiency and success?

As enterprises begin to scale artificial intelligence (AI) pilot programs, which often incorporate deep learning (DL), machine learning (ML), and natural language processing (NLP), across the enterprise, the need for a high-performance
compute and storage environment becomes clear. HPC environments, with their massive processing capability and low-latency access to data, are seen by many observers as a clear complementary technology to AI.

What are the top AI use cases that are likely to be powered by HPC systems now, and in the future?

What technological and operational challenges exist with deploying HPC systems that can support AI pilot programs or full-scale rollouts?

What operational models are enterprises using to deploy AI technology that is powered by HPC

What security and regulatory issues need to be considered when using HPC systems to power AI use cases?

Machines, like children, only model what they've been taught. Machines start out bias free but can quickly learn and even amplify bad human behavior. Learn how to mitigate bias by first understanding how it is introduced
and then by being intentional about its removal. After removing bias, introduce transparency in how predictions are made and even expose how good an algorithm is at making predictions. Attend this talk to learn how
you can start to build systems that are bias free. S. A. M. (Suspicious Activity Monitor), a predictive policing algorithm, is used as the case study during this talk.

Attendees will learn:

How human bias is passed on to machines

Practical strategies for identifying and removing bias

Tips for making machine learning algorithms transparent

4:00 PANEL: Digital Transformation in Financial Services

The financial services industry is one of the most progressive enterprise verticals adopting AI technologies for use in internal business applications and customer-facing services. Intelligent automation enables the financial
services field to grow and scale beyond the power of human intelligence. This is accomplished by optimizing existing business processes and enhancing the way that financial organizations engage with external customers.
Panelists representing insurance, banking, mortgage, and investment segments discuss how intelligent automation is changing the financial services organization to compete in the data-driven economy. Attendees to this
panel will learn:

Can intelligent automation bring back the human connection?

Why clean, trusted, and governed data is essential for AI to succeed

How are financial service organizations measuring the impact from intelligent automation?

Organizations are under pressure to reduce operating costs while increasing output quality and capacity. RPA promises fast increases in productivity. AI is real but does it complement RPA? Where do these technologies
converge to deliver Digital Process Automation? Attend this session to learn about:

The difference between rules-based automation and intelligent RPA

Is the goal employee productivity, replacement or something more strategic?

Artificial intelligence (AI) and intelligent automation are starting to impact the legal field. In many cases, it is augmenting the repetitive, procedural work being done and empowering professionals to
focus on complex tasks. Corporate legal teams as well as independent law firms gain from these advances. Attend this talk to hear:

How AI-powered software is improving the legal document analysis process, including discovery, fact checking, and due diligence

Artificial Intelligence (AI) is an enabling horizontal technology. While the field of AI is not new, an international standards committee looking at the entire AI ecosystem is a recent development.
ISO/IEC JTC 1/SC 42 is the first of its kind international standards committee that is looking at the entire AI ecosystem. To enable mass deployment and adoption of AI in the commercial and industrial
fields, standards are required. Attendees to this session will learn:

How common terminology can be used by all stakeholders to enable clear communication and sound decision making

Which use cases, their requirements, and best practices for application of the technology will guide technology development.

Like other transformational IT technologies, how pervasive AI requires addressing issues of trustworthiness from the get-go.

Why standardization of algorithms and computational techniques will allow a higher level of adoption, use, and interoperability.

1:30 Recognizing Early Signals of Breakthrough Innovation and Disruption Using Artificial Intelligence and Machine Learning

InveniAI is leveraging AI and ML to harness terabytes of disparate data sets to recognize complex patterns and unlock value. The company’s platform operates in real-time and recognizes
these patterns in a rapidly changing and diverse data environment by engaging internal experts to personalize the definition of success and failure for an organization or vertical market
to identify emerging science and innovation across diseases, drugs, targets, and pathways among others. The platform eliminates dependence on a time-series and uses industry-aware scoring
algorithms that are customized and further strengthened by incorporating continuous feedback through machine learning.